OpenRaiser/Intern-Atlas huggingface dataset:
Preferential subspace identification (PSID) with forward-backward smoothing
Venue: arXiv cs.LG 2025
Year: 2025

System identification methods for multivariate time-series, such as neural and behavioral recordings, have been used to build models for predicting one from the other. For example, Preferential Subspace Identification (PSID) builds a state-space model of a primary time-series (e.g., neural activity) to optimally predict a secondary time-series (e.g., behavior). However, PSID focuses on optimal prediction using past primary data, even though in offline applications, better estimation can be achieved by incorporating concurrent data (filtering) or all available data (smoothing). Here, we extend PSID to enable optimal filtering and smoothing. First, we show that the presence of a secondary signal makes it possible to uniquely identify a model with an optimal Kalman update step (to enable filtering) from a family of otherwise equivalent state-space models. Our filtering solution augments PSID with a reduced-rank regression step that directly learns the optimal gain required for the update step from data. We refer to this extension of PSID as PSID with filtering. Second, inspired by two-filter Kalman smoother formulations, we develop a novel forward-backward PSID smoothing algorithm where we first apply PSID with filtering and then apply it again in the reverse time direction on the residuals of the filtered secondary signal. We validate our methods on simulated data, showing that our approach recovers the ground-truth model parameters for filtering, and achieves optimal filtering and smoothing decoding performance of the secondary signal that matches the ideal performance of the true underlying model. This work provides a principled framework for optimal linear filtering and smoothing in the two-signal setting, significantly expanding the toolkit for analyzing dynamic interactions in multivariate time-series.
MeMo: Attentional Momentum for Real-time Audio-visual Speaker Extraction under Impaired Visual Conditions
Venue: arXiv cs.SD 2025
Year: 2025

Audio-visual Target Speaker Extraction (AV-TSE) aims to isolate a target speaker's voice from multi-speaker environments by leveraging visual cues as guidance. However, the performance of AV-TSE systems heavily relies on the quality of these visual cues. In extreme scenarios where visual cues are missing or severely degraded, the system may fail to accurately extract the target speaker. In contrast, humans can maintain attention on a target speaker even in the absence of explicit auxiliary information. Motivated by such human cognitive ability, we propose a novel framework called MeMo, which incorporates two adaptive memory banks to store attention-related information. MeMo is specifically designed for real-time scenarios: once initial attention is established, the system maintains attentional momentum over time, even when visual cues become unavailable. We conduct comprehensive experiments to verify the effectiveness of MeMo. Experimental results demonstrate that our proposed framework achieves SI-SNR improvements of at least 2 dB over the corresponding baseline.
Metaphor and Large Language Models: When Surface Features Matter More than Deep Understanding
Venue: arXiv cs.CL 2025
Year: 2025

This paper presents a comprehensive evaluation of the capabilities of Large Language Models (LLMs) in metaphor interpretation across multiple datasets, tasks, and prompt configurations. Although metaphor processing has gained significant attention in Natural Language Processing (NLP), previous research has been limited to single-dataset evaluations and specific task settings, often using artificially constructed data through lexical replacement. We address these limitations by conducting extensive experiments using diverse publicly available datasets with inference and metaphor annotations, focusing on Natural Language Inference (NLI) and Question Answering (QA) tasks. The results indicate that LLMs' performance is more influenced by features like lexical overlap and sentence length than by metaphorical content, demonstrating that any alleged emergent abilities of LLMs to understand metaphorical language are the result of a combination of surface-level features, in-context learning, and linguistic knowledge. This work provides critical insights into the current capabilities and limitations of LLMs in processing figurative language, highlighting the need for more realistic evaluation frameworks in metaphor interpretation tasks. Data and code are publicly available.
AlgoSimBench: Identifying Algorithmically Similar Problems for Competitive Programming
Venue: arXiv cs.CL 2025
Year: 2025

Recent progress in LLMs, such as reasoning models, has demonstrated strong abilities to solve complex competitive programming problems, often rivaling top human competitors. However, it remains underexplored whether these abilities generalize to relevant domains that are less seen during training. To address this, we introduce AlgoSimBench, a new benchmark designed to assess LLMs' ability to identify algorithmically similar problems (ASPs)-problems that can be solved using similar algorithmic approaches. AlgoSimBench consists of 1317 problems, annotated with 231 distinct fine-grained algorithm tags, from which we curate 402 multiple-choice questions (MCQs), where each question presents one algorithmically similar problem alongside three textually similar but algorithmically dissimilar distractors. Our evaluation reveals that LLMs struggle to identify ASPs, with the best-performing model (o3-mini) achieving only 65.9% accuracy on the MCQ task. To address this challenge, we propose attempted solution matching (ASM), a novel method for improving problem similarity detection. On our MCQ task, ASM yields an absolute accuracy improvement of 6.7% to 11.7% across different models. We also evaluated code embedding models and retrieval methods on similar problem identification. While the adversarial selection of problems degrades the performance to be less than random, we found that simply summarizing the problem to remove narrative elements eliminates the effect, and combining ASM with a keyword-prioritized method, BM25, can yield up to 52.2% accuracy. Code and data are available at github.com
SurgX: Neuron-Concept Association for Explainable Surgical Phase Recognition
Venue: arXiv cs.CV 2025
Year: 2025

Surgical phase recognition plays a crucial role in surgical workflow analysis, enabling various applications such as surgical monitoring, skill assessment, and workflow optimization. Despite significant advancements in deep learning-based surgical phase recognition, these models remain inherently opaque, making it difficult to understand how they make decisions. This lack of interpretability hinders trust and makes it challenging to debug the model. To address this challenge, we propose SurgX, a novel concept-based explanation framework that enhances the interpretability of surgical phase recognition models by associating neurons with relevant concepts. In this paper, we introduce the process of selecting representative example sequences for neurons, constructing a concept set tailored to the surgical video dataset, associating neurons with concepts and identifying neurons crucial for predictions. Through extensive experiments on two surgical phase recognition models, we validate our method and analyze the explanation for prediction. This highlights the potential of our method in explaining surgical phase recognition. The code is available at https://github.com/ailab-kyunghee/SurgX
EgoPrune: Efficient Token Pruning for Egomotion Video Reasoning in Embodied Agent
Venue: arXiv cs.CV 2025
Year: 2025

Egomotion videos are first-person recordings where the view changes continuously due to the agent's movement. As they serve as the primary visual input for embodied AI agents, making egomotion video reasoning more efficient is therefore essential for real-world deployment. Recent advances in vision-language models have enabled strong multimodal reasoning capabilities, but their computational cost remains prohibitive for long, redundant video inputs. Existing token pruning methods, typically designed for third-person videos, fail to leverage the spatiotemporal continuity and motion constraints inherent in egomotion settings. To address this, we propose EgoPrune, a training-free token pruning method tailored for egomotion video reasoning. EgoPrune comprises three components: a keyframe selector adapted from EmbodiedR for temporally efficient sampling; Perspective-Aware Redundancy Filtering (PARF), which aligns visual tokens using perspective transformations and removes redundant tokens; and a Maximal Marginal Relevance (MMR)-based token selector that jointly considers visual-text relevance and intra-frame diversity. Experiments on two egomotion video benchmarks show that EgoPrune consistently outperforms prior training-free methods across various pruning ratios while significantly reducing FLOPs, memory usage, and latency. Moreover, we deploy EgoPrune on an embodied agent equipped with a Jetson Orin NX 16GB edge device, demonstrating its real-world efficiency and suitability for on-device egomotion video reasoning.
Job Scheduling under Base and Additional Fees, with Applications to Mixed-Criticality Scheduling
Venue: arXiv cs.DS 2025
Year: 2025

We are concerned with the problem of scheduling $n$ jobs onto $m$ identical machines. Each machine has to be in operation for a prescribed time, and the objective is to minimize the total machine working time. Precisely, let $c_i$ be the prescribed time for machine $i$, where $i\in[m]$, and $p_j$ be the processing time for job $j$, where $j\in[n]$. The problem asks for a schedule $σ\colon\, J\to M$ such that $\sum_{i=1}^m\max\{c_i, \sum_{j\inσ^{-1}(i)}p_j\}$ is minimized, where $J$ and $M$ denote the sets of jobs and machines, respectively. We show that First Fit Decreasing (FFD) leads to a $1.5$-approximation, and this problem admits a polynomial-time approximation scheme (PTAS). The idea is further applied to mixed-criticality system scheduling to yield improved approximation results.
Challenging Disability and Interaction Norms in XR: Cooling Down the Empathy Machine in Waiting for Hands
Venue: arXiv cs.HC 2025
Year: 2025

Virtual Reality (VR) is often described as the "ultimate empathy machine," framing disability as an experience to be simulated through such technologies, which can reduce disability to a spectacle of pity or inspiration. In response, we present Waiting for Hands (WfH), an interactive eXtended Reality (XR) installation that critiques this logic by: (1) repurposing interaction norms in XR through the creation of Alternative Controllers, and (2) staging an absurd XR performance using the built controllers to disrupt sentimentalized disability narratives. The performance involves eight people: two XR participants on stage and six audience members watching a projected documentary about Hema Kumari, an Indian singer living with Rheumatoid Arthritis. The XR users partially obscure the film, drawing attention through strange mouth and hand movements performed in XR. This creates a layered experience that disrupts direct engagement with Hema's story and introduces uncertainty. While XR is often seen as a fully immersive, sensory-dominant medium, this piece subverts that framing by using XR to produce absurdity and alienation. By challenging empathy-driven and pitiable narratives of disability, we ask what ethical stance an XR performance can take to attune participants to non-normative embodiment while resisting spectacle.
An aerial color image anomaly dataset for search missions in complex forested terrain
Venue: arXiv cs.CV 2025
Year: 2025

After a family murder in rural Germany, authorities failed to locate the suspect in a vast forest despite a massive search. To aid the search, a research aircraft captured high-resolution aerial imagery. Due to dense vegetation obscuring small clues, automated analysis was ineffective, prompting a crowd-search initiative. This effort produced a unique dataset of labeled, hard-to-detect anomalies under occluded, real-world conditions. It can serve as a benchmark for improving anomaly detection approaches in complex forest environments, supporting manhunts and rescue operations. Initial benchmark tests showed existing methods performed poorly, highlighting the need for context-aware approaches. The dataset is openly accessible for offline processing. An additional interactive web interface supports online viewing and dynamic growth by allowing users to annotate and submit new findings.
FollowUpBot: An LLM-Based Conversational Robot for Automatic Postoperative Follow-up
Venue: arXiv cs.HC 2025
Year: 2025

Postoperative follow-up plays a crucial role in monitoring recovery and identifying complications. However, traditional approaches, typically involving bedside interviews and manual documentation, are time-consuming and labor-intensive. Although existing digital solutions, such as web questionnaires and intelligent automated calls, can alleviate the workload of nurses to a certain extent, they either deliver an inflexible scripted interaction or face private information leakage issues. To address these limitations, this paper introduces FollowUpBot, an LLM-powered edge-deployed robot for postoperative care and monitoring. It allows dynamic planning of optimal routes and uses edge-deployed LLMs to conduct adaptive and face-to-face conversations with patients through multiple interaction modes, ensuring data privacy. Moreover, FollowUpBot is capable of automatically generating structured postoperative follow-up reports for healthcare institutions by analyzing patient interactions during follow-up. Experimental results demonstrate that our robot achieves high coverage and satisfaction in follow-up interactions, as well as high report generation accuracy across diverse field types. The demonstration video is available at https://www.youtube.com/watch?v=_uFgDO7NoK0.
Bayesian Separation Logic
Venue: arXiv cs.PL 2025
Year: 2025

Bayesian probabilistic programming languages (BPPLs) let users denote statistical models as code while the interpreter infers the posterior distribution. The semantics of BPPLs are usually mathematically complex and unable to reason about desirable properties such as expected values and independence of random variables. To reason about these properties in a non-Bayesian setting, probabilistic separation logics such as PSL and Lilac interpret separating conjunction as probabilistic independence of random variables. However, no existing separation logic can handle Bayesian updating, which is the key distinguishing feature of BPPLs. To close this gap, we introduce Bayesian separation logic (BaSL), a probabilistic separation logic that gives semantics to BPPL. We prove an internal version of Bayes' theorem using a result in measure theory known as the Rokhlin-Simmons disintegration theorem. Consequently, BaSL can model probabilistic programming concepts such as Bayesian updating, unnormalised distribution, conditional distribution, soft constraint, conjugate prior and improper prior while maintaining modularity via the frame rule. The model of BaSL is based on a novel instantiation of Kripke resource monoid via $σ$-finite measure spaces over the Hilbert cube, and the semantics of Hoare triple is compatible with an existing denotational semantics of BPPL based on the category of $s$-finite kernels. Using BaSL, we then prove properties of statistical models such as the expected value of Bayesian coin flip, correlation of random variables in the collider Bayesian network, the posterior distributions of the burglar alarm model, a parameter estimation algorithm, and the Gaussian mixture model.
Procedure Learning via Regularized Gromov-Wasserstein Optimal Transport
Venue: arXiv cs.CV 2025
Year: 2025

We study self-supervised procedure learning, which discovers key steps and their order from a set of unlabeled videos. Previous methods typically learn frame-to-frame correspondences between videos before determining key steps and their order. However, their performance often suffers from order variations, background/redundant frames, and repeated actions. To overcome these challenges, we propose a self-supervised framework, which utilizes a fused Gromov-Wasserstein optimal transport with a structural prior for frame-to-frame mapping. However, optimizing only for the above temporal alignment may lead to degenerate solutions, where all frames are mapped to a small cluster in the embedding space and thus every video is assigned to just one key step. To address that issue, we integrate a contrastive regularization, which maps different frames to various points, avoiding trivial solutions. Finally, extensive experiments on egocentric and third-person benchmarks demonstrate our superior performance over prior works, including OPEL which relies on a classical Kantorovich optimal transport with an optimality prior.
Data Aware Differentiable Neural Architecture Search for Tiny Keyword Spotting Applications
Venue: arXiv cs.LG 2025
Year: 2025

The success of Machine Learning is increasingly tempered by its significant resource footprint, driving interest in efficient paradigms like TinyML. However, the inherent complexity of designing TinyML systems hampers their broad adoption. To reduce this complexity, we introduce "Data Aware Differentiable Neural Architecture Search". Unlike conventional Differentiable Neural Architecture Search, our approach expands the search space to include data configuration parameters alongside architectural choices. This enables Data Aware Differentiable Neural Architecture Search to co-optimize model architecture and input data characteristics, effectively balancing resource usage and system performance for TinyML applications. Initial results on keyword spotting demonstrate that this novel approach to TinyML system design can generate lean but highly accurate systems.
Compress-Align-Detect: onboard change detection from unregistered images
Venue: arXiv cs.CV 2025
Year: 2025

Change detection from satellite images typically incurs a delay ranging from several hours up to days because of latency in downlinking the acquired images and generating orthorectified image products at the ground stations; this may preclude real- or near real-time applications. To overcome this limitation, we propose shifting the entire change detection workflow onboard satellites. This requires to simultaneously solve challenges in data storage, image registration and change detection with a strict complexity constraint. In this paper, we present a novel and efficient framework for onboard change detection that addresses the aforementioned challenges in an end-to-end fashion with a deep neural network composed of three interlinked submodules: (1) image compression, tailored to minimize onboard data storage resources; (2) lightweight co-registration of non-orthorectified multi-temporal image pairs; and (3) a novel temporally-invariant and computationally efficient change detection model. This is the first approach in the literature combining all these tasks in a single end-to-end framework with the constraints dictated by onboard processing. Experimental results compare each submodule with the current state-of-the-art, and evaluate the performance of the overall integrated system in realistic setting on low-power hardware. Compelling change detection results are obtained in terms of F1 score as a function of compression rate, sustaining a throughput of 0.7 Mpixel/s on a 15W accelerator.
Drafting the Landscape of Computational Musicology Tools: a Survey-Based Approach
Venue: arXiv cs.DL 2025
Year: 2025

Since the 60s, musicology has been increasingly impacted by computational tools in various ways, from systematic analysis approaches to modeling of creativity. This article presents a comprehensive assessment of the current state of Computational Musicology tools based on survey data collected from practitioners in the field. We gathered information on tool usage patterns, common analytical tasks, user satisfaction levels, data characteristics, and prioritized features across four distinct domains: symbolic music, music-related imagery, audio, and text. Our findings reveal significant gaps between current tooling capabilities and user needs, highlighting some limitations of these tools across all domains. This assessment contributes to the ongoing dialogue between tool developers and music scholars, aiming to enhance the effectiveness and accessibility of computational methods in musicological research.
When Pipelined In-Memory Accelerators Meet Spiking Direct Feedback Alignment: A Co-Design for Neuromorphic Edge Computing
Venue: arXiv cs.AR 2025
Year: 2025

Spiking Neural Networks (SNNs) are increasingly favored for deployment on resource-constrained edge devices due to their energy-efficient and event-driven processing capabilities. However, training SNNs remains challenging because of the computational intensity of traditional backpropagation algorithms adapted for spike-based systems. In this paper, we propose a novel software-hardware co-design that introduces a hardware-friendly training algorithm, Spiking Direct Feedback Alignment (SDFA) and implement it on a Resistive Random Access Memory (RRAM)-based In-Memory Computing (IMC) architecture, referred to as PipeSDFA, to accelerate SNN training. Software-wise, the computational complexity of SNN training is reduced by the SDFA through the elimination of sequential error propagation. Hardware-wise, a three-level pipelined dataflow is designed based on IMC architecture to parallelize the training process. Experimental results demonstrate that the PipeSDFA training accelerator incurs less than 2% accuracy loss on five datasets compared to baselines, while achieving 1.1X~10.5X and 1.37X~2.1X reductions in training time and energy consumption, respectively compared to PipeLayer.
Estimation of Payload Inertial Parameters from Human Demonstrations by Hand Guiding
Venue: arXiv cs.RO 2025
Year: 2025

As the availability of cobots increases, it is essential to address the needs of users with little to no programming knowledge to operate such systems efficiently. Programming concepts often use intuitive interaction modalities, such as hand guiding, to address this. When programming in-contact motions, such frameworks require knowledge of the robot tool's payload inertial parameters (PIP) in addition to the demonstrated velocities and forces to ensure effective hybrid motion-force control. This paper aims to enable non-expert users to program in-contact motions more efficiently by eliminating the need for a dedicated PIP calibration, thereby enabling flexible robot tool changes. Since demonstrated tasks generally also contain motions with non-contact, our approach uses these parts to estimate the robot's PIP using established estimation techniques. The results show that the estimation of the payload's mass is accurate, whereas the center of mass and the inertia tensor are affected by noise and a lack of excitation. Overall, these findings show the feasibility of PIP estimation during hand guiding but also highlight the need for sufficient payload accelerations for an accurate estimation.